A new evolutionary optimization algorithm inspired by Plant Life Cycle

In this paper a new evolutionary optimization algorithm inspired by Plant Life Cycle is proposed. Plants are quite successful in reproduction and searching new habitats despite of their mobility limitations. During millions of years plants continued to innovate new ways of exploiting other animals and peripheral conditions and using them as part of their reproduction cycle. Thanks to these innovations, they not only produce new generations but effectively do local as well as global search in their environment. The proposed algorithm uses general concepts in plant life cycle to form an optimization method. It searches problem space in six steps named: pollination, fertilization, seed production, seed dispersal, local competition and selection to find best possible answer. The proposed algorithm has been implemented on some known test functions and simulation results of proposed algorithm are compared with Genetic algorithm and Cuckoo search algorithm results. Results demonstrate the superiority of new algorithm in most tests over two other algorithms in searching the solution space especially in high dimensions.